Agents
Personalized Instance-based Navigation Toward User-Specific Objects in Realistic Environments
To address these limitations, we propose a new task denominated Personalized Instance-based Navigation (PIN), in which an embodied agent is tasked with locating and reaching a specific personal object by distinguishing it among multiple instances of the same category.
Online Relational Inference for Evolving Multi-agent Interacting Systems
Table 1: Comparison of key features between prior works and this work. Method Description Model Consider Evolution in Criteria Agnostic Interaction Parameter Dynamics Acc.
On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games
In this paper, we formalize a novel reinforcement learning model which explicitly represents the information structure.
AGILE: A Novel Reinforcement Learning Framework of LLM Agents
The agent possesses capabilities beyond conversation, including reflection, tool usage, and expert consultation.
Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning Hang Zhou 1,2, Yehui Tang
Unfortunately, collecting high-quality and diverse data is both expensive and time-consuming. To mitigate this issue, we propose a novel Star-Agents framework, which automates the enhancement of data quality across datasets through multi-agent collaboration and assessment. The framework adopts a three-pronged strategy.
Polynomial-Time Computation of Exact Φ-Equilibria in Polyhedral Games
Contrary to the Nash equilibrium, in a correlated equilibrium the players' strategies are correlated by a fictitious mediator that can recommend (but not enforce)